Overview

Dataset statistics

Number of variables15
Number of observations115064
Missing cells127817
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 MiB
Average record size in memory121.0 B

Variable types

Numeric12
Categorical2
Boolean1

Warnings

MarkDown1 is highly correlated with MarkDown4High correlation
MarkDown4 is highly correlated with MarkDown1High correlation
Size is highly correlated with MarkDown1 and 1 other fieldsHigh correlation
MarkDown1 is highly correlated with Size and 2 other fieldsHigh correlation
MarkDown4 is highly correlated with MarkDown1High correlation
MarkDown5 is highly correlated with Size and 1 other fieldsHigh correlation
Unemployment is highly correlated with Store and 3 other fieldsHigh correlation
MarkDown4 is highly correlated with MarkDown1 and 1 other fieldsHigh correlation
Store is highly correlated with Unemployment and 4 other fieldsHigh correlation
IsHoliday is highly correlated with MarkDown1 and 2 other fieldsHigh correlation
Size is highly correlated with Unemployment and 3 other fieldsHigh correlation
MarkDown1 is highly correlated with MarkDown4 and 2 other fieldsHigh correlation
MarkDown3 is highly correlated with IsHoliday and 1 other fieldsHigh correlation
Fuel_Price is highly correlated with Unemployment and 4 other fieldsHigh correlation
MarkDown2 is highly correlated with DateHigh correlation
CPI is highly correlated with Unemployment and 4 other fieldsHigh correlation
Temperature is highly correlated with Fuel_Price and 2 other fieldsHigh correlation
Type is highly correlated with Store and 1 other fieldsHigh correlation
Date is highly correlated with MarkDown4 and 6 other fieldsHigh correlation
Date is highly correlated with IsHolidayHigh correlation
IsHoliday is highly correlated with DateHigh correlation
MarkDown2 has 28627 (24.9%) missing values Missing
MarkDown3 has 9829 (8.5%) missing values Missing
MarkDown4 has 12888 (11.2%) missing values Missing
CPI has 38162 (33.2%) missing values Missing
Unemployment has 38162 (33.2%) missing values Missing
MarkDown5 is highly skewed (γ1 = 37.97681524) Skewed
Date is uniformly distributed Uniform

Reproduction

Analysis started2022-06-05 15:27:11.020156
Analysis finished2022-06-05 15:31:06.214742
Duration3 minutes and 55.19 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Store
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.23820656
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-06-05T21:01:06.855757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile43
Maximum45
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.80992959
Coefficient of variation (CV)0.5760324941
Kurtosis-1.149779815
Mean22.23820656
Median Absolute Deviation (MAD)11
Skewness0.07677311858
Sum2558817
Variance164.0942961
MonotonicityIncreasing
2022-06-05T21:01:07.654756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
132836
 
2.5%
42803
 
2.4%
192799
 
2.4%
22797
 
2.4%
272791
 
2.4%
242790
 
2.4%
62788
 
2.4%
12783
 
2.4%
102782
 
2.4%
202774
 
2.4%
Other values (35)87121
75.7%
ValueCountFrequency (%)
12783
2.4%
22797
2.4%
32473
2.1%
42803
2.4%
52447
2.1%
62788
2.4%
72669
2.3%
82699
2.3%
92435
2.1%
102782
2.4%
ValueCountFrequency (%)
452626
2.3%
442072
1.8%
431863
1.6%
421962
1.7%
412754
2.4%
402738
2.4%
392704
2.3%
381987
1.7%
372013
1.7%
361718
1.5%

Dept
Real number (ℝ≥0)

Distinct81
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.33952409
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-06-05T21:01:08.548756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q118
median37
Q374
95-th percentile95
Maximum99
Range98
Interquartile range (IQR)56

Descriptive statistics

Standard deviation30.65641013
Coefficient of variation (CV)0.6914014248
Kurtosis-1.224242898
Mean44.33952409
Median Absolute Deviation (MAD)23
Skewness0.3624198542
Sum5101883
Variance939.8154821
MonotonicityNot monotonic
2022-06-05T21:01:09.482826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11755
 
1.5%
161755
 
1.5%
811755
 
1.5%
671755
 
1.5%
821755
 
1.5%
461755
 
1.5%
21755
 
1.5%
401755
 
1.5%
381755
 
1.5%
901755
 
1.5%
Other values (71)97514
84.7%
ValueCountFrequency (%)
11755
1.5%
21755
1.5%
31755
1.5%
41755
1.5%
51738
1.5%
61577
1.4%
71755
1.5%
81755
1.5%
91754
1.5%
101755
1.5%
ValueCountFrequency (%)
99613
 
0.5%
981632
1.4%
971716
1.5%
961350
1.2%
951755
1.5%
941464
1.3%
931638
1.4%
921755
1.5%
911755
1.5%
901755
1.5%

Date
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2012-12-21
 
3002
2012-12-07
 
2989
2012-12-28
 
2988
2012-12-14
 
2986
2013-02-15
 
2984
Other values (34)
100115 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1150640
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012-11-02
2nd row2012-11-02
3rd row2012-11-02
4th row2012-11-02
5th row2012-11-02

Common Values

ValueCountFrequency (%)
2012-12-213002
 
2.6%
2012-12-072989
 
2.6%
2012-12-282988
 
2.6%
2012-12-142986
 
2.6%
2013-02-152984
 
2.6%
2012-11-232976
 
2.6%
2012-11-092971
 
2.6%
2013-01-042964
 
2.6%
2013-02-082964
 
2.6%
2012-11-302962
 
2.6%
Other values (29)85278
74.1%

Length

2022-06-05T21:01:11.230757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-12-213002
 
2.6%
2012-12-072989
 
2.6%
2012-12-282988
 
2.6%
2012-12-142986
 
2.6%
2013-02-152984
 
2.6%
2012-11-232976
 
2.6%
2012-11-092971
 
2.6%
2013-01-042964
 
2.6%
2013-02-082964
 
2.6%
2012-11-302962
 
2.6%
Other values (29)85278
74.1%

Most occurring characters

ValueCountFrequency (%)
0244692
21.3%
-230128
20.0%
1224528
19.5%
2215739
18.7%
3114767
10.0%
529470
 
2.6%
423610
 
2.1%
720617
 
1.8%
620542
 
1.8%
814749
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number920512
80.0%
Dash Punctuation230128
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0244692
26.6%
1224528
24.4%
2215739
23.4%
3114767
12.5%
529470
 
3.2%
423610
 
2.6%
720617
 
2.2%
620542
 
2.2%
814749
 
1.6%
911798
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
-230128
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1150640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0244692
21.3%
-230128
20.0%
1224528
19.5%
2215739
18.7%
3114767
10.0%
529470
 
2.6%
423610
 
2.1%
720617
 
1.8%
620542
 
1.8%
814749
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1150640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0244692
21.3%
-230128
20.0%
1224528
19.5%
2215739
18.7%
3114767
10.0%
529470
 
2.6%
423610
 
2.1%
720617
 
1.8%
620542
 
1.8%
814749
 
1.3%

IsHoliday
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.3 KiB
False
106136 
True
 
8928
ValueCountFrequency (%)
False106136
92.2%
True8928
 
7.8%
2022-06-05T21:01:11.999746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
A
58713 
B
44500 
C
11851 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters115064
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A58713
51.0%
B44500
38.7%
C11851
 
10.3%

Length

2022-06-05T21:01:14.103750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-05T21:01:14.581824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
a58713
51.0%
b44500
38.7%
c11851
 
10.3%

Most occurring characters

ValueCountFrequency (%)
A58713
51.0%
B44500
38.7%
C11851
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter115064
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A58713
51.0%
B44500
38.7%
C11851
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Latin115064
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A58713
51.0%
B44500
38.7%
C11851
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII115064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A58713
51.0%
B44500
38.7%
C11851
 
10.3%

Size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136497.6889
Minimum34875
Maximum219622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-06-05T21:01:15.279739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum34875
5-th percentile39690
Q193638
median140167
Q3202505
95-th percentile206302
Maximum219622
Range184747
Interquartile range (IQR)108867

Descriptive statistics

Standard deviation61106.92644
Coefficient of variation (CV)0.4476773704
Kurtosis-1.214415882
Mean136497.6889
Median Absolute Deviation (MAD)62140
Skewness-0.3219495188
Sum1.570597008 × 1010
Variance3734056459
MonotonicityNot monotonic
2022-06-05T21:01:16.109740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
399105803
 
5.0%
396905702
 
5.0%
2038195589
 
4.9%
2196222836
 
2.5%
2058632803
 
2.4%
2023072797
 
2.4%
2041842791
 
2.4%
2025052788
 
2.4%
1513152783
 
2.4%
1265122782
 
2.4%
Other values (30)78390
68.1%
ValueCountFrequency (%)
348752447
2.1%
373922473
2.1%
396905702
5.0%
399105803
5.0%
410621863
 
1.6%
429881954
 
1.7%
571972614
2.3%
707132669
2.3%
931882682
2.3%
936382554
2.2%
ValueCountFrequency (%)
2196222836
2.5%
2074992756
2.4%
2063022745
2.4%
2058632803
2.4%
2041842791
2.4%
2038195589
4.9%
2037502754
2.4%
2037422774
2.4%
2030072757
2.4%
2025052788
2.4%

Temperature
Real number (ℝ)

HIGH CORRELATION

Distinct1236
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.94180387
Minimum-7.29
Maximum101.95
Zeros0
Zeros (%)0.0%
Negative208
Negative (%)0.2%
Memory size1.8 MiB
2022-06-05T21:01:17.570755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.29
5-th percentile23.98
Q139.82
median54.47
Q367.35
95-th percentile83.82
Maximum101.95
Range109.24
Interquartile range (IQR)27.53

Descriptive statistics

Standard deviation18.72415289
Coefficient of variation (CV)0.3471176629
Kurtosis-0.4959672256
Mean53.94180387
Median Absolute Deviation (MAD)13.79
Skewness-0.07357032708
Sum6206759.72
Variance350.5939014
MonotonicityNot monotonic
2022-06-05T21:01:18.700744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.25312
 
0.3%
70.18309
 
0.3%
70.74309
 
0.3%
38.95272
 
0.2%
70.01263
 
0.2%
57.87262
 
0.2%
85261
 
0.2%
52.38260
 
0.2%
79.15260
 
0.2%
58.66259
 
0.2%
Other values (1226)112297
97.6%
ValueCountFrequency (%)
-7.2969
0.1%
-6.6169
0.1%
-6.0870
0.1%
0.2568
0.1%
2.3271
0.1%
2.4570
0.1%
470
0.1%
6.2768
0.1%
8.5569
0.1%
8.8266
0.1%
ValueCountFrequency (%)
101.95187
0.2%
99.6648
 
< 0.1%
95.5145
 
< 0.1%
95.145
 
< 0.1%
94.145
 
< 0.1%
93.75188
0.2%
93.6244
 
< 0.1%
93.54121
0.1%
93.1747
 
< 0.1%
92.9845
 
< 0.1%

Fuel_Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct297
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.581546322
Minimum2.872
Maximum4.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-06-05T21:01:19.819746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.872
5-th percentile3.161
Q13.431
median3.606
Q33.766
95-th percentile3.951
Maximum4.125
Range1.253
Interquartile range (IQR)0.335

Descriptive statistics

Standard deviation0.2394419304
Coefficient of variation (CV)0.06685434414
Kurtosis-0.1175964872
Mean3.581546322
Median Absolute Deviation (MAD)0.167
Skewness-0.391281194
Sum412107.046
Variance0.05733243801
MonotonicityNot monotonic
2022-06-05T21:01:20.775819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4171853
 
1.6%
3.5831851
 
1.6%
3.3861793
 
1.6%
3.6111374
 
1.2%
3.1081201
 
1.0%
3.4791169
 
1.0%
3.5971071
 
0.9%
3.4511043
 
0.9%
3.2271040
 
0.9%
3.6141028
 
0.9%
Other values (287)101641
88.3%
ValueCountFrequency (%)
2.872276
0.2%
2.889276
0.2%
2.914193
0.2%
2.927194
0.2%
2.957279
0.2%
2.982195
0.2%
2.998282
0.2%
3.029195
0.2%
3.034278
0.2%
3.066197
0.2%
ValueCountFrequency (%)
4.125166
 
0.1%
4.109189
 
0.2%
4.104186
 
0.2%
4.099355
0.3%
4.079282
0.2%
4.062351
0.3%
4.045569
0.5%
4.031358
0.3%
4.029281
0.2%
4.016358
0.3%

MarkDown1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1752
Distinct (%)1.5%
Missing149
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7689.216439
Minimum-2781.45
Maximum103184.98
Zeros0
Zeros (%)0.0%
Negative207
Negative (%)0.2%
Memory size1.8 MiB
2022-06-05T21:01:21.871821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2781.45
5-th percentile189.49
Q11966.46
median4842.29
Q39439.14
95-th percentile23140.88
Maximum103184.98
Range105966.43
Interquartile range (IQR)7472.68

Descriptive statistics

Standard deviation10698.76072
Coefficient of variation (CV)1.391398044
Kurtosis22.87146067
Mean7689.216439
Median Absolute Deviation (MAD)3402.05
Skewness4.172741821
Sum883606307.1
Variance114463480.9
MonotonicityNot monotonic
2022-06-05T21:01:23.224747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10755.5774
 
0.1%
9753.8874
 
0.1%
5692.6674
 
0.1%
13613.5274
 
0.1%
20297.674
 
0.1%
4655.5574
 
0.1%
22673.1174
 
0.1%
13357.3174
 
0.1%
3365.6173
 
0.1%
4642.3773
 
0.1%
Other values (1742)114177
99.2%
(Missing)149
 
0.1%
ValueCountFrequency (%)
-2781.4550
< 0.1%
-772.2143
< 0.1%
-563.970
0.1%
-16.9344
< 0.1%
2.1446
< 0.1%
2.562
0.1%
2.8244
< 0.1%
449
< 0.1%
4.6249
< 0.1%
5.7649
< 0.1%
ValueCountFrequency (%)
103184.9872
0.1%
95102.571
0.1%
88750.3466
0.1%
84139.3672
0.1%
80498.6571
0.1%
77017.2468
0.1%
75522.8670
0.1%
74777.2265
0.1%
73407.4973
0.1%
72937.2973
0.1%

MarkDown2
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1257
Distinct (%)1.5%
Missing28627
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean3734.051729
Minimum-35.74
Maximum71074.17
Zeros0
Zeros (%)0.0%
Negative412
Negative (%)0.4%
Memory size1.8 MiB
2022-06-05T21:01:24.290740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-35.74
5-th percentile6.14
Q1180.35
median742.59
Q32735.67
95-th percentile22671.67
Maximum71074.17
Range71109.91
Interquartile range (IQR)2555.32

Descriptive statistics

Standard deviation8323.495014
Coefficient of variation (CV)2.229078657
Kurtosis15.88072536
Mean3734.051729
Median Absolute Deviation (MAD)667.49
Skewness3.740647282
Sum322760229.3
Variance69280569.25
MonotonicityNot monotonic
2022-06-05T21:01:26.976744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01346
 
0.3%
0.03340
 
0.3%
82.92217
 
0.2%
11214
 
0.2%
3209
 
0.2%
4191
 
0.2%
104.92141
 
0.1%
1.49138
 
0.1%
0.06138
 
0.1%
7.5137
 
0.1%
Other values (1247)84366
73.3%
(Missing)28627
 
24.9%
ValueCountFrequency (%)
-35.7463
 
0.1%
-15.4571
 
0.1%
-7.7665
 
0.1%
-3.2769
 
0.1%
-0.0573
 
0.1%
-0.0171
 
0.1%
0.01346
0.3%
0.02135
 
0.1%
0.03340
0.3%
0.0472
 
0.1%
ValueCountFrequency (%)
71074.1772
0.1%
59362.372
0.1%
56549.6973
0.1%
52850.7174
0.1%
52304.8773
0.1%
50636.7172
0.1%
49026.373
0.1%
47440.5171
0.1%
47382.7271
0.1%
46315.8470
0.1%

MarkDown3
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1421
Distinct (%)1.4%
Missing9829
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean2403.088666
Minimum-179.26
Maximum149483.31
Zeros0
Zeros (%)0.0%
Negative589
Negative (%)0.5%
Memory size1.8 MiB
2022-06-05T21:01:28.713735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-179.26
5-th percentile1.18
Q115.1
median78.26
Q3272.58
95-th percentile2361.57
Maximum149483.31
Range149662.57
Interquartile range (IQR)257.48

Descriptive statistics

Standard deviation13767.93931
Coefficient of variation (CV)5.729268132
Kurtosis54.09068129
Mean2403.088666
Median Absolute Deviation (MAD)73.28
Skewness7.146137688
Sum252889035.8
Variance189556152.9
MonotonicityNot monotonic
2022-06-05T21:01:29.701756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2599
 
0.5%
1498
 
0.4%
0.6419
 
0.4%
0.8348
 
0.3%
2324
 
0.3%
0.4278
 
0.2%
0.2272
 
0.2%
3271
 
0.2%
5271
 
0.2%
0.1269
 
0.2%
Other values (1411)101686
88.4%
(Missing)9829
 
8.5%
ValueCountFrequency (%)
-179.2662
0.1%
-89.166
0.1%
-44.5467
0.1%
-23.9772
0.1%
-17.4469
0.1%
-14.2963
0.1%
-2.5867
0.1%
-0.8671
0.1%
-0.7352
< 0.1%
0.0662
0.1%
ValueCountFrequency (%)
149483.3173
0.1%
146394.4472
0.1%
139621.5172
0.1%
130129.1170
0.1%
115048.8173
0.1%
112255.6772
0.1%
109976.1470
0.1%
105691.6772
0.1%
105146.372
0.1%
97533.472
0.1%

MarkDown4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1483
Distinct (%)1.5%
Missing12888
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean3356.219071
Minimum0.22
Maximum65344.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-06-05T21:01:30.833758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile16.96
Q1155.46
median840.94
Q33096.92
95-th percentile14191.01
Maximum65344.64
Range65344.42
Interquartile range (IQR)2941.46

Descriptive statistics

Standard deviation7570.501545
Coefficient of variation (CV)2.255663705
Kurtosis25.45178529
Mean3356.219071
Median Absolute Deviation (MAD)796.5
Skewness4.66858587
Sum342925039.8
Variance57312493.64
MonotonicityNot monotonic
2022-06-05T21:01:32.143747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3171
 
0.1%
0.63154
 
0.1%
358.15145
 
0.1%
55.46142
 
0.1%
2.61141
 
0.1%
3.97138
 
0.1%
4.88137
 
0.1%
27.44136
 
0.1%
970.77134
 
0.1%
1.92120
 
0.1%
Other values (1473)100758
87.6%
(Missing)12888
 
11.2%
ValueCountFrequency (%)
0.2256
 
< 0.1%
0.63154
0.1%
0.6646
 
< 0.1%
0.7854
 
< 0.1%
1.2643
 
< 0.1%
1.4769
0.1%
1.5652
 
< 0.1%
1.7448
 
< 0.1%
1.8955
 
< 0.1%
1.92120
0.1%
ValueCountFrequency (%)
65344.6472
0.1%
63830.9171
0.1%
63130.8170
0.1%
60065.8272
0.1%
56735.2572
0.1%
56600.9771
0.1%
52850.865
0.1%
51587.0372
0.1%
49809.368
0.1%
49781.2169
0.1%

MarkDown5
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct1754
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3922.681189
Minimum-185.17
Maximum771448.1
Zeros0
Zeros (%)0.0%
Negative136
Negative (%)0.1%
Memory size1.8 MiB
2022-06-05T21:01:33.274760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-185.17
5-th percentile540.89
Q11309.3
median2390.43
Q34227.27
95-th percentile9316.71
Maximum771448.1
Range771633.27
Interquartile range (IQR)2917.97

Descriptive statistics

Standard deviation19445.15075
Coefficient of variation (CV)4.957107093
Kurtosis1494.90905
Mean3922.681189
Median Absolute Deviation (MAD)1288.67
Skewness37.97681524
Sum451359388.3
Variance378113887.5
MonotonicityNot monotonic
2022-06-05T21:01:34.311743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3113.78137
 
0.1%
5449.9874
 
0.1%
22677.9174
 
0.1%
18831.3474
 
0.1%
2105.1474
 
0.1%
21807.9974
 
0.1%
2167.7374
 
0.1%
1947.2574
 
0.1%
7968.2874
 
0.1%
1993.573
 
0.1%
Other values (1744)114262
99.3%
ValueCountFrequency (%)
-185.1763
0.1%
-37.0273
0.1%
40.9844
< 0.1%
60.9265
0.1%
114.2551
< 0.1%
134.4749
< 0.1%
142.7566
0.1%
149.8766
0.1%
157.4865
0.1%
163.0146
< 0.1%
ValueCountFrequency (%)
771448.171
0.1%
45648.8869
0.1%
45050.5570
0.1%
43336.3470
0.1%
35238.9872
0.1%
31990.7469
0.1%
23963.3869
0.1%
23525.3670
0.1%
22677.9174
0.1%
22400.6569
0.1%

CPI
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct360
Distinct (%)0.5%
Missing38162
Missing (%)33.2%
Infinite0
Infinite (%)0.0%
Mean176.9613466
Minimum131.2362258
Maximum228.9764563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-06-05T21:01:35.423819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum131.2362258
5-th percentile131.4784
Q1138.4020333
median192.3044449
Q3223.2445318
95-th percentile227.7847527
Maximum228.9764563
Range97.7402305
Interquartile range (IQR)84.8424985

Descriptive statistics

Standard deviation41.2399666
Coefficient of variation (CV)0.2330450541
Kurtosis-1.858841625
Mean176.9613466
Median Absolute Deviation (MAD)36.4254189
Skewness0.07144841262
Sum13608681.48
Variance1700.734845
MonotonicityNot monotonic
2022-06-05T21:01:36.590743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132.71609682080
 
1.8%
139.12261291664
 
1.4%
201.0705712825
 
0.7%
224.8025314783
 
0.7%
131.537704
 
0.6%
132.2725714703
 
0.6%
131.2793548702
 
0.6%
131.642702
 
0.6%
131.4784701
 
0.6%
132.5914516698
 
0.6%
Other values (350)67340
58.5%
(Missing)38162
33.2%
ValueCountFrequency (%)
131.2362258695
0.6%
131.2793548702
0.6%
131.3258696
0.6%
131.3766667695
0.6%
131.4275333693
0.6%
131.4784701
0.6%
131.537704
0.6%
131.642702
0.6%
131.747695
0.6%
131.852696
0.6%
ValueCountFrequency (%)
228.9764563186
0.2%
228.889248260
 
0.1%
228.802040160
 
0.1%
228.7796682208
0.2%
228.7298638401
0.3%
228.71483262
 
0.1%
228.692645668
 
0.1%
228.6428882134
 
0.1%
228.627623963
 
0.1%
228.60562368
 
0.1%

Unemployment
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct89
Distinct (%)0.1%
Missing38162
Missing (%)33.2%
Infinite0
Infinite (%)0.0%
Mean6.868733167
Minimum3.684
Maximum10.199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-06-05T21:01:37.575744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.684
5-th percentile3.932
Q15.771
median6.806
Q38.036
95-th percentile9.91
Maximum10.199
Range6.515
Interquartile range (IQR)2.265

Descriptive statistics

Standard deviation1.583427353
Coefficient of variation (CV)0.2305268402
Kurtosis-0.6093291493
Mean6.868733167
Median Absolute Deviation (MAD)1.186
Skewness0.1414012336
Sum528219.318
Variance2.507242182
MonotonicityNot monotonic
2022-06-05T21:01:38.401819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2373377
 
2.9%
9.912454
 
2.1%
6.172336
 
2.0%
6.2662147
 
1.9%
5.3721871
 
1.6%
8.0361823
 
1.6%
3.9321808
 
1.6%
7.1071808
 
1.6%
7.4391805
 
1.6%
8.6251783
 
1.5%
Other values (79)55690
48.4%
(Missing)38162
33.2%
ValueCountFrequency (%)
3.684556
 
0.5%
3.879650
 
0.6%
3.896288
 
0.3%
3.921932
0.8%
3.9321808
1.6%
4.1451268
1.1%
4.872210
 
0.2%
4.893246
 
0.2%
4.954573
 
0.5%
4.983700
 
0.6%
ValueCountFrequency (%)
10.1991731
1.5%
9.912454
2.1%
9.874751
 
0.7%
9.151588
 
0.5%
8.951847
 
0.7%
8.9341529
1.3%
8.8391063
0.9%
8.796476
 
0.4%
8.78257
 
0.2%
8.75928
 
0.8%

Interactions

2022-06-05T20:58:21.236079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:22.160080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:23.546089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:24.666097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:25.616079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:27.045077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:28.074093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:28.965093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:29.852080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:30.957083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:32.421084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:33.354079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:34.406092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:35.998080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:37.329082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:38.420079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:39.480156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:40.364156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:41.645073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:42.932079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:44.142098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:45.034094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:45.920157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:46.815083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:48.154079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:49.228090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:50.163088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:51.161155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:52.099154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:53.275088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:54.486244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:55.874461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:57.486201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:58.893654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:58:59.985436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:01.241758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:02.450756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:03.533777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:04.501758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:05.630757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:06.808758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:07.678756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:08.553777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:10.249769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:11.081832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:11.993755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:12.931757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:13.641832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:14.440770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:15.308833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:16.134769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:17.039756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:17.848756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:18.701756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:19.483834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:20.324836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:21.167834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:22.181757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:23.496749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:24.468756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:25.488756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:26.516770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:27.431832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:28.425759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:30.016757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:31.071759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:32.081759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:33.247836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:34.155835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:35.051833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:36.152838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:37.333771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:38.355770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:39.615759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:40.692834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:41.797756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:42.824756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:43.753764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:44.707770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:45.589759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:48.234750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:49.293835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:50.354776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:51.259750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:52.284760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:53.411835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:54.485777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:55.566755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:56.576754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:57.601768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:58.612773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T20:59:59.834768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:00.830771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:01.776754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:03.293750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:04.243760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:05.306756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:06.180834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:07.047756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:08.315269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:09.368046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:10.636271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:12.222962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:13.499628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:14.563039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:15.756153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:16.607141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:17.466452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:18.483143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:20.002736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:21.146757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:22.314756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:23.267820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:24.369747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:25.407821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:26.711747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:28.190746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:30.023743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:31.259746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:32.381766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:33.288743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:34.051757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:35.130744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:36.336819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:38.556843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:39.556743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:40.918741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:41.859737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:42.813744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:43.788763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:45.114745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:46.085737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:47.129745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:48.452735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:49.596756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:50.610822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:51.415743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:52.200821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:52.958822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:53.765819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:54.580743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:55.415743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:56.271819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-05T21:00:57.019822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-06-05T21:01:39.307765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-05T21:01:41.184750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-05T21:01:42.549740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-05T21:01:43.768760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-06-05T21:01:45.205743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-06-05T21:00:58.309822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-05T21:01:00.733821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-05T21:01:04.113821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-05T21:01:05.172818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

StoreDeptDateIsHolidayTypeSizeTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemployment
0112012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573
1122012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573
2132012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573
3142012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573
4152012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573
5162012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573
6172012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573
7182012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573
8192012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573
91102012-11-02FalseA15131555.323.3866766.445147.750.823639.92737.42223.4627796.573

Last rows

StoreDeptDateIsHolidayTypeSizeTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemployment
11505445852013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN
11505545872013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN
11505645902013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN
11505745912013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN
11505845922013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN
11505945932013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN
11506045942013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN
11506145952013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN
11506245972013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN
11506345982013-07-26FalseB11822176.063.804212.02851.732.0610.881864.57NaNNaN